内容简介

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

作品目录

Introduction

Part I: Foundations

A gentle start

A formal learning model

Learning via uniform convergence

The bias-complexity trade-off

The VC-dimension

Non-uniform learnability

The runtime of learning

Part II: From Theory to Algorithms

Linear predictors

Boosting

Model selection and validation

Convex learning problems

Regularization and stability

Stochastic gradient descent

Support vector machines

Kernel methods

Multiclass, ranking, and complex prediction problems

Decision trees

Nearest neighbor

Neural networks

Part III: Additional Learning Models

Online learning

Clustering

Dimensionality reduction

Generative models

Feature selection and generation

Part IV: Advanced Theory

Rademacher complexities

Covering numbers

Proof of the fundamental theorem of learning theory

Multiclass learnability

Compression bounds

PAC-Bayes

Appendices

Technical lemmas

Measure concentration

Linear algebra